from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Tuple   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Union[str, Any]=13   ,   lowerCamelCase_						:						Optional[int]=7   ,   lowerCamelCase_						:						Any=True   ,   lowerCamelCase_						:						str=True   ,   lowerCamelCase_						:						Optional[Any]=True   ,   lowerCamelCase_						:						Tuple=True   ,   lowerCamelCase_						:						List[str]=99   ,   lowerCamelCase_						:						List[str]=32   ,   lowerCamelCase_						:						str=2   ,   lowerCamelCase_						:						Any=4   ,   lowerCamelCase_						:						Optional[Any]=37   ,   lowerCamelCase_						:						Any="gelu"   ,   lowerCamelCase_						:						List[Any]=0.1   ,   lowerCamelCase_						:						List[Any]=0.1   ,   lowerCamelCase_						:						Optional[Any]=512   ,   lowerCamelCase_						:						List[Any]=16   ,   lowerCamelCase_						:						str=2   ,   lowerCamelCase_						:						Any=0.0_2   ,   lowerCamelCase_						:						Any=False   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						List[str]="None"   ,   lowerCamelCase_						:						Dict=3   ,   lowerCamelCase_						:						Union[str, Any]=4   ,   lowerCamelCase_						:						Any=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      seq_length
    UpperCamelCase			  =      is_training
    UpperCamelCase			  =      use_input_mask
    UpperCamelCase			  =      use_token_type_ids
    UpperCamelCase			  =      use_labels
    UpperCamelCase			  =      vocab_size
    UpperCamelCase			  =      hidden_size
    UpperCamelCase			  =      num_hidden_layers
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      intermediate_size
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      hidden_dropout_prob
    UpperCamelCase			  =      attention_probs_dropout_prob
    UpperCamelCase			  =      max_position_embeddings
    UpperCamelCase			  =      type_vocab_size
    UpperCamelCase			  =      type_sequence_label_size
    UpperCamelCase			  =      initializer_range
    UpperCamelCase			  =      num_labels
    UpperCamelCase			  =      num_choices
    UpperCamelCase			  =      relative_attention
    UpperCamelCase			  =      position_biased_input
    UpperCamelCase			  =      pos_att_type
    UpperCamelCase			  =      scope







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size				)

    UpperCamelCase			  =      None
    if self.use_input_mask:
     UpperCamelCase			  =      random_attention_mask([self.batch_size, self.seq_length]				)

    UpperCamelCase			  =      None
    if self.use_token_type_ids:
     UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size				)

    UpperCamelCase			  =      None
    UpperCamelCase			  =      None
    UpperCamelCase			  =      None
    if self.use_labels:
     UpperCamelCase			  =      ids_tensor([self.batch_size]   ,   self.type_sequence_label_size				)
     UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels				)

    UpperCamelCase			  =      DebertaVaConfig(
        vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   relative_attention=self.relative_attention   ,   position_biased_input=self.position_biased_input   ,   initializer_range=self.initializer_range   ,   return_dict=lowerCamelCase_   ,   )

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      TFDebertaVaModel(config=lowerCamelCase_				)
    UpperCamelCase			  =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

    UpperCamelCase			  =      [input_ids, input_mask]
    UpperCamelCase			  =      model(lowerCamelCase_				)

    UpperCamelCase			  =      model(lowerCamelCase_				)

    self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      TFDebertaVaForMaskedLM(config=lowerCamelCase_				)
    UpperCamelCase			  =      {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    UpperCamelCase			  =      model(lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)				)







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.num_labels
    UpperCamelCase			  =      TFDebertaVaForSequenceClassification(config=lowerCamelCase_				)
    UpperCamelCase			  =      {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    UpperCamelCase			  =      model(lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)				)







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self.num_labels
    UpperCamelCase			  =      TFDebertaVaForTokenClassification(config=lowerCamelCase_				)
    UpperCamelCase			  =      {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    UpperCamelCase			  =      model(lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      TFDebertaVaForQuestionAnswering(config=lowerCamelCase_				)
    UpperCamelCase			  =      {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    UpperCamelCase			  =      model(lowerCamelCase_				)
    self.parent.assertEqual(result.start_logits.shape   ,   (self.batch_size, self.seq_length)				)
    self.parent.assertEqual(result.end_logits.shape   ,   (self.batch_size, self.seq_length)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.prepare_config_and_inputs()
    (
        (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     
    )			  =      config_and_inputs
    UpperCamelCase			  =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict





@require_tf
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				(
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase           =				(
       {
           """feature-extraction""": TFDebertaVaModel,
           """fill-mask""": TFDebertaVaForMaskedLM,
           """question-answering""": TFDebertaVaForQuestionAnswering,
           """text-classification""": TFDebertaVaForSequenceClassification,
           """token-classification""": TFDebertaVaForTokenClassification,
           """zero-shot""": TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      TFDebertaVaModelTester(self				)
    UpperCamelCase			  =      ConfigTester(self   ,   config_class=lowerCamelCase_   ,   hidden_size=37				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    self.config_tester.run_common_tests()







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_				)







   @slow
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""				)
    self.assertIsNotNone(lowerCamelCase_				)





@require_tf
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   @unittest.skip(reason="""Model not available yet"""				)
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    pass







   @slow
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""				)
    UpperCamelCase			  =      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				)
    UpperCamelCase			  =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				)[0]

    UpperCamelCase			  =      tf.constant(
        [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]				)
    tf.debugging.assert_near(output[:, 1:4, 1:4]   ,   lowerCamelCase_   ,   atol=1E-4				)

from __future__ import annotations
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      text, pattern
    UpperCamelCase  ,     UpperCamelCase			  =      len(lowerCamelCase_				), len(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if char == self.pattern[i]:
      return i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if self.pattern[i] != self.text[current_pos + i]:
      return current_pos + i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for i in range(self.textLen - self.patLen + 1				):
     UpperCamelCase			  =      self.mismatch_in_text(lowerCamelCase_				)
     if mismatch_index == -1:
      positions.append(lowerCamelCase_				)
     else:
      UpperCamelCase			  =      self.match_in_pattern(self.text[mismatch_index]				)
      UpperCamelCase			  =      (
          mismatch_index - match_index
      )  # shifting index lgtm [py/multiple-definition]
    return positions





_SCREAMING_SNAKE_CASE 		=      """ABAABA"""
_SCREAMING_SNAKE_CASE 		=      """AB"""
_SCREAMING_SNAKE_CASE 		=      BoyerMooreSearch(text, pattern)
_SCREAMING_SNAKE_CASE 		=      bms.bad_character_heuristic()

if len(positions) == 0:
     print("""No match found""")
else:
     print("""Pattern found in following positions: """)
     print(positions)
